Why Major Sports Have Embraced Big Data (And How They Use It)

Major Sports Are Embracing Big Data Analytics

In the post-Moneyball sports world, stories about using advanced analytics to help make front office and coaching decisions began to multiply.

There are endless tales of New York Yankees manager Joe Girardi and his binder filled to the margins with data.

In the NBA, the Portland Trail Blazers are living up to their name, hiring analytics managers to look deep into metrics to make adjustments. The Jacksonville Jaguars now have an analytics department that just helped the team figure out which college player to choose in the first round of last month’s NFL Draft.

There are franchises that realize they can get a leg up on the competition if they utilize the insights provided by advanced statistics. But according to experts, teams in major sports leagues – and in the amateur ranks, too – have only begun to figure out how to utilize the kinds of stats and analytics provided by Big Data.

Take the NBA , where some teams like the Toronto Raptors utilize SportVU cameras that track their players’ movements all over the court to provide minute details about hoopsters on the hardwood. But as Mike Boyle of The Sports Analytics Institute pointed out, though teams have access to the data generated by those cameras, they aren’t necessarily able to digest it to its fullest extent. “When you introduce the concepts of true Big Data, which Netflix, Google or Facebook processes and turns into valuable resources to a company, then you go from [hiring] stats people to needing engineers who can develop the bigger systems,” Boyle says.

Boyle, along with SAI co-founder Kevin Mongeon, consults with a number of NHL teams and use the company’s data warehouse to help generate probabilities, such as how often “a shot will result in a goal,” as their site says. Boyle says there is even a metric called Player Lifetime Value that predicts a team member’s future contributions, but he explains that franchises are not yet up to speed on how to digest the increased amount of complex data these systems can produce.

James Piette agrees. He’s the vice president of analytics for Krossover, a company that takes game film in basketball, football and lacrosse, breaks it down to one play at a time and puts together an advanced analysis to go along with it. Their services focus on the amateur market, listing high schools and colleges like Harvard and Northeastern as clients.

“To say that [sports] people are embracing data, as in, ‘I’m willing to take data and understand the value of it,’ they’re getting there,” Piette says. “The people reporting it are doing the basic measures on it. They’re barely scratching the surface of what really that data can talk about. A lot of it’s because … they don’t have the sophistication to understand what questions you should ask.”

The reality in sports is that while coaches and general managers are relying more on analytics, like player evaluations or the right plays to draw up at key moments in games, it’s still just a part of the equation. But it may not be too far into the future that teams may start investing more heavily into the brainpower that can provide them a big advantage with big data.

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